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app.py

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+import streamlit as st
+import google.generativeai as genai
+from langchain_community.vectorstores import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
+from langchain_core.prompts import PromptTemplate
+from langchain.chains.question_answering import load_qa_chain
+import fitz
+from dotenv import load_dotenv
+import os
+load_dotenv()
+
+genai.configure(api_key=os.getenv('GOOGLE_API_KEY'))
+st.title('Document Q&A')
+data_uploaded=False
+
+def get_chain():
+    model = ChatGoogleGenerativeAI(model='gemini-1.5-pro-latest', temperature=0.1)
+    prompt_ = """
+    Answer the questions as detailed as possible from the provided context, make sure to provide all the 
+        details, if the answer is not in the provided context just say, "answer is not available in context",   
+        don't provide the wrong answer\n. 
+        context: {context}
+        Questions:{question}
+        Answer:
+    """
+    prompt = PromptTemplate(template=prompt_, input_variables=["context", "question"])
+    chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+    return chain
+
+
+def get_pdf_content(pdffile):
+    with fitz.open(stream=pdffile.read(), filetype="pdf") as doc:
+        text = ""
+        for page in doc:
+            text += page.get_text()
+    return text
+
+
+def create_database(data):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    final_document = text_splitter.split_text(data)
+    vectors = FAISS.from_texts(final_document, embeddings)
+    vectors.save_local("faiss_index")
+
+
+def user_input(u_question):
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    new_db = FAISS.load_local('faiss_index', embeddings, allow_dangerous_deserialization=True)
+    docs = new_db.similarity_search(u_question)
+    chain = get_chain()
+    response = chain(
+        {"input_documents": docs, "question": u_question}, return_only_outputs=True
+    )
+    return response["output_text"]
+
+
+with st.sidebar:
+    uploaded_file = st.file_uploader("Upload pdf file", key="pdf_uploader")
+    if st.button('Create vector store'):
+        if uploaded_file is not None:
+            data = get_pdf_content(uploaded_file)
+            create_database(data)
+            st.write("Vector store created")
+        else:
+            st.write("Please upload pdf file")
+
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+
+if prompt := st.chat_input("Ask questions"):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    with st.chat_message("assistant"):
+        message_placeholder = st.empty()
+        with st.spinner(text="Fetching details..."):
+            response = user_input(prompt)
+        message_placeholder.markdown(response)
+    st.session_state.messages.append({"role": "assistant", "content": response})

requirements.txt

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+streamlit==1.36.0
+langchain==0.2.3
+langchain-google-genai==1.0.7
+langchain-community==0.2.4
+langchain-core==0.2.10
+PyMuPDF==1.24.7
+faiss-cpu==1.8.0